Automatic Background Removal and Correction of Systematic Error Caused by Noise Expecting Bio-Raman Big Data Analysis

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Data Science

Background:

• Spectral data preprocessing, including background removal, is essential for Raman spectroscopy.
• Automated methods are needed to handle large datasets efficiently.

Purpose of the Study:

• To propose a practical method for correcting systematic errors caused by noise in spectral measurements.
• To enhance the accuracy and effectiveness of automated background removal in Raman spectroscopy.

Main Methods:

• Developed an automated background removal method considering the shortest spectrum length via scaling factor adjustment.
• Implemented a novel correction for systematic errors arising from measurement noise.

Main Results:

• The proposed noise correction method proved effective and accurate.
• The enhanced method improves the overall performance of automatic background removal.

Conclusions:

• The developed noise correction technique offers a practical solution for improving spectral data quality.
• This advancement facilitates more reliable advanced spectral analysis in Raman spectroscopy.